受强制性开放获取政策约束的文章 - Bo Liu了解详情
无法在其他位置公开访问的文章：3 篇
Hierarchical Feature Selection for Random Projection
Q Wang, J Wan, F Nie, B Liu, C Young, X Li
IEEE Transactions on Neural Networks and Learning Systems (IEEE-TNNLS), 2018
强制性开放获取政策： 中国科学院, 国家自然科学基金委员会
A lightweight multi-scale aggregated model for detecting aerial images captured by UAVs
Z Li, X Liu, Y Zhao, B Liu, Z Huang, R Hong
Journal of Visual Communication and Image Representation 77, 103058, 2021
强制性开放获取政策： 国家自然科学基金委员会
TOPS: Transition-Based Volatility-Reduced Policy Search
L Xu, D Lyu, Y Pan, A Jiang, B Liu
International Conference on Autonomous Agents and Multiagent Systems, 3-47, 2022
强制性开放获取政策： US National Science Foundation
可在其他位置公开访问的文章：15 篇
GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values
S Zhang, B Liu, S Whiteson
International Conference on Machine Learning (ICML), 2020
强制性开放获取政策： US National Science Foundation, UK Engineering and Physical Sciences …
Learning correlated communication topology in multi-agent reinforcement learning
Y Du, B Liu, V Moens, Z Liu, Z Ren, J Wang, X Chen, H Zhang
Proceedings of the 20th International Conference on Autonomous Agents and …, 2021
强制性开放获取政策： 中国科学院
Transferable contextual bandit for cross-domain recommendation
B Liu, Y Wei, Y Zhang, Z Yan, Q Yang
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) 32 (1), 2018
强制性开放获取政策： 国家自然科学基金委员会
Uncorrelated Group LASSO
D Kong, J Liu, B Liu, X Bao
The Thirtieth AAAI Conference on Artificial Intelligence (AAAI), 2016
强制性开放获取政策： US National Science Foundation
Zero-shot learning from adversarial feature residual to compact visual feature
B Liu, Q Dong, Z Hu
Proceedings of the AAAI Conference on Artificial Intelligence 34 (07), 11547 …, 2020
强制性开放获取政策： 中国科学院, 国家自然科学基金委员会
Model credibility revisited: Concepts and considerations for appropriate trust
L Yilmaz, B Liu
Journal of Simulation 16 (3), 312-325, 2022
强制性开放获取政策： US National Science Foundation
Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity
B Liu, I Gemp, M Ghamvamzadeh, J Liu, S Mahadevan, M Petrik
Journal of Artificial Intelligence Research (JAIR), 2018
强制性开放获取政策： US National Science Foundation
TDM: Trustworthy Decision-Making Via Interpretability Enhancement
D Lyu, F Yang, H Kwon, W Dong, L Yilmaz, B Liu
IEEE Transactions on Emerging Topics in Computational Intelligence (IEEE-TETCI), 2021
强制性开放获取政策： US National Science Foundation
Stable and Efficient Policy Evaluation
D Lyu, B Liu, M Geist, W Dong, S Biaz, Q Wang
IEEE Transactions on Neural Networks and Learning Systems (IEEE-TNNLS), 2018
强制性开放获取政策： US National Science Foundation, US Department of Defense
Self-supervised multi-scale pyramid fusion networks for realistic bokeh effect rendering
Z Wang, A Jiang, C Zhang, H Li, B Liu
Journal of Visual Communication and Image Representation, 103580, 2022
强制性开放获取政策： 国家自然科学基金委员会
Deep residual refining based pseudo‐multi‐frame network for effective single image super‐resolution
K Mei, A Jiang, J Li, B Liu, J Ye, M Wang
IET Image Processing 13 (4), 591-599, 2019
强制性开放获取政策： 国家自然科学基金委员会
Offline reinforcement learning for price-based demand response program design
C Xu, B Liu, Y Zhao
2023 57th Annual Conference on Information Sciences and Systems (CISS), 1-6, 2023
强制性开放获取政策： US National Science Foundation
Restoration algorithm for noisy complex illumination
Z Liu, T Gao, F Kong, Z Jiao, A Yang, S Li, B Liu
IET Computer Vision 13 (2), 224-232, 2019
强制性开放获取政策： 国家自然科学基金委员会
Deep multimodal reinforcement network with contextually guided recurrent attention for image question answering
AW Jiang, B Liu, MW Wang
Journal of Computer Science and Technology 32, 738-748, 2017
强制性开放获取政策： 国家自然科学基金委员会
Dantzig Selector with an Approximately Optimal Denoising Matrix and its Application in Sparse Reinforcement Learning
B Liu, L Zhang, J Liu
32nd Conference on Uncertainty in Artificial Intelligence (UAI), 2016
强制性开放获取政策： US National Science Foundation
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